Our videos are a step-by-step journey across the fundamental topics in Artificial Intelligence and Machine Learning. Each video has a corresponding tutorial and interactive code-demo, which are important tools for learning how to apply the concepts explained.

Machine Learning Intro and Applications

This is the first video in the series. This video covers the essential background for understanding supervised learning, regression, and classification.

Difficulty - Beginner

Keywords - Regression, Classification, Models

Loss and Gradient Descent

This is the second video in the series. This video goes over linear models, loss, hyperparameters, stochastic and batch gradient descent, multidimensional gradient descent, etc.

Difficulty - Intermediate

Keywords - Linear Regression, Loss, Gradient Descent

Coding a ML Regression Program in Scikit-Learn

This is the third video in the series. This video goes over Scikit-Learn, NumPy, SciPy, ML Libraries, Coding in Sklearn,  data visualization

Difficulty - Intermediate

Keywords - Scikit-Learn, NumPy, SciPy

ML Classifiers and Decision Trees

This is the fourth video in the series. This video goes over Classifiers, Decision Trees, KNN, Artificial Neural Nets, Naive Bayes, Lazy/Eager Learners, Building a Decision Tree, Overfitting, Underfitting, Pruning a Decision Tree, Coding a DT in Scikit-learn

Difficulty - Beginner

Keywords - Decision Tree, Pruning, Fitting

Support Vector Machines and Logistic Regression

This is the fifth video in the series. This video goes over Logistic Regression, Sigmoid Function, Decision Boundaries, Probability boundaries, Cross-Entropy Loss, Support Vector Machines, Hyperplane, Margin, Kernel Transformations, Hinge Loss, Gamma Parameters, Regularization Tuning 

Difficulty - Advanced 

Keywords - Support Vector Machine, Hyperplane, Kernels

Dimensionality Reduction, PCA, Linear Discriminant Analysis

This is the sixth video in the series. This video goes over limitations of logistic regression, linear discriminant analysis, learning LDA, dimensionality reduction, projection into lower dimension spaces, overfitting with higher dimensional data, Principal Components Analysis, PCA example, and visualization

Difficulty - Advanced 

Keywords - PCA, Linear Discriminant Analysis, Dimensions

Title: Naïve Bayes and Machine Learning Pipelines

This is the seventh video in the series. This video goes over Bayesian Machine Learning, Conditional Probability, Bayes Theorem, Multivariate distributions, Prior Probability, Posterior Probability, Gaussian PDF, Machine Learning Pipelines, chaining of an ML pipeline

Difficulty - Intermediate

Keywords - Bayesian ML, Probability, Pipelines

Random Forest Models and K-nearest Neighbors

This is the eight video in the series. This video goes over Random Forest Classifiers, Advantages, Disadvantages, K-nearest neighbors classifier, choosing "K", balancing error, sci-kit learn implementation

Difficulty - Intermediate

Keywords - Random Forest, K-nearest Neighbors

Tree Search, Dynamic Programming, Uniform Cost Search

This is the ninth video in the series. This video goes over goes over graph algorithms, reflex v. state models, search trees, nodes/edge cost, MCP, graph traversal, backtracking, BFS, DFS, Dynamic Programming, State graphs, Uniform Cost Search (UCS), Space and Time Complexity

Difficulty - Advanced

Keywords - Searching Algorithms, Higher Logic ML